Overview

Dataset statistics

Number of variables16
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory132.0 B

Variable types

Numeric13
Categorical3

Alerts

kmeans_cluster has constant value ""Constant
dbsscan_cluster has constant value ""Constant
dbscan_labels has constant value ""Constant
Ash is highly overall correlated with Ash_Alcanity and 1 other fieldsHigh correlation
Ash_Alcanity is highly overall correlated with AshHigh correlation
Color_Intensity is highly overall correlated with HueHigh correlation
Flavanoids is highly overall correlated with Proanthocyanins and 1 other fieldsHigh correlation
Hue is highly overall correlated with Color_IntensityHigh correlation
Magnesium is highly overall correlated with ProlineHigh correlation
Proanthocyanins is highly overall correlated with FlavanoidsHigh correlation
Proline is highly overall correlated with MagnesiumHigh correlation
Total_Phenols is highly overall correlated with Ash and 1 other fieldsHigh correlation
Alcohol has unique valuesUnique
Malic_Acid has unique valuesUnique
Total_Phenols has 1 (3.4%) zerosZeros
Flavanoids has 1 (3.4%) zerosZeros
OD280 has 1 (3.4%) zerosZeros

Reproduction

Analysis started2023-11-29 06:59:19.524815
Analysis finished2023-11-29 07:00:09.217319
Duration49.69 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

Alcohol
Real number (ℝ)

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57867514
Minimum0.30789474
Maximum0.82368421
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-29T07:00:09.370530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.30789474
5-th percentile0.32526316
Q10.47631579
median0.60263158
Q30.7
95-th percentile0.78947368
Maximum0.82368421
Range0.51578947
Interquartile range (IQR)0.22368421

Descriptive statistics

Standard deviation0.14789505
Coefficient of variation (CV)0.25557526
Kurtosis-0.81153559
Mean0.57867514
Median Absolute Deviation (MAD)0.10789474
Skewness-0.30476384
Sum16.781579
Variance0.021872945
MonotonicityNot monotonic
2023-11-29T07:00:09.694093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.3315789474 1
 
3.4%
0.5078947368 1
 
3.4%
0.5631578947 1
 
3.4%
0.5894736842 1
 
3.4%
0.6236842105 1
 
3.4%
0.7052631579 1
 
3.4%
0.8236842105 1
 
3.4%
0.4578947368 1
 
3.4%
0.3078947368 1
 
3.4%
0.6710526316 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
0.3078947368 1
3.4%
0.3210526316 1
3.4%
0.3315789474 1
3.4%
0.35 1
3.4%
0.4078947368 1
3.4%
0.4578947368 1
3.4%
0.4710526316 1
3.4%
0.4763157895 1
3.4%
0.4842105263 1
3.4%
0.5 1
3.4%
ValueCountFrequency (%)
0.8236842105 1
3.4%
0.8157894737 1
3.4%
0.75 1
3.4%
0.7394736842 1
3.4%
0.7236842105 1
3.4%
0.7105263158 1
3.4%
0.7052631579 1
3.4%
0.7 1
3.4%
0.6815789474 1
3.4%
0.6710526316 1
3.4%

Malic_Acid
Real number (ℝ)

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58573798
Minimum0.12359551
Maximum0.96629213
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-29T07:00:09.996322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.12359551
5-th percentile0.25568411
Q10.41573034
median0.56629213
Q30.75505618
95-th percentile0.92539326
Maximum0.96629213
Range0.84269663
Interquartile range (IQR)0.33932584

Descriptive statistics

Standard deviation0.21416531
Coefficient of variation (CV)0.36563329
Kurtosis-0.42615397
Mean0.58573798
Median Absolute Deviation (MAD)0.15280899
Skewness-0.077669488
Sum16.986401
Variance0.045866778
MonotonicityNot monotonic
2023-11-29T07:00:10.294560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.195505618 1
 
3.4%
0.608988764 1
 
3.4%
0.4157303371 1
 
3.4%
0.795505618 1
 
3.4%
0.7123595506 1
 
3.4%
0.3459518459 1
 
3.4%
0.397752809 1
 
3.4%
0.3707865169 1
 
3.4%
0.5146067416 1
 
3.4%
0.4134831461 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
0.1235955056 1
3.4%
0.195505618 1
3.4%
0.3459518459 1
3.4%
0.3707865169 1
3.4%
0.397752809 1
3.4%
0.408988764 1
3.4%
0.4134831461 1
3.4%
0.4157303371 1
3.4%
0.4539325843 1
3.4%
0.4651685393 1
3.4%
ValueCountFrequency (%)
0.9662921348 1
3.4%
0.9460674157 1
3.4%
0.8943820225 1
3.4%
0.8696629213 1
3.4%
0.8134831461 1
3.4%
0.795505618 1
3.4%
0.7595505618 1
3.4%
0.7550561798 1
3.4%
0.7123595506 1
3.4%
0.6943820225 1
3.4%

Ash
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58507631
Minimum0.32786885
Maximum0.85245902
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-29T07:00:10.572286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.32786885
5-th percentile0.38852459
Q10.49180328
median0.55737705
Q30.68852459
95-th percentile0.8295082
Maximum0.85245902
Range0.52459016
Interquartile range (IQR)0.19672131

Descriptive statistics

Standard deviation0.14186002
Coefficient of variation (CV)0.24246413
Kurtosis-0.62675264
Mean0.58507631
Median Absolute Deviation (MAD)0.081967213
Skewness0.35918197
Sum16.967213
Variance0.020124265
MonotonicityNot monotonic
2023-11-29T07:00:10.830963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.5327868852 3
 
10.3%
0.6393442623 3
 
10.3%
0.5573770492 3
 
10.3%
0.6885245902 2
 
6.9%
0.4918032787 2
 
6.9%
0.4590163934 2
 
6.9%
0.4180327869 1
 
3.4%
0.737704918 1
 
3.4%
0.5491803279 1
 
3.4%
0.6147540984 1
 
3.4%
Other values (10) 10
34.5%
ValueCountFrequency (%)
0.3278688525 1
 
3.4%
0.368852459 1
 
3.4%
0.4180327869 1
 
3.4%
0.4344262295 1
 
3.4%
0.4590163934 2
6.9%
0.4754098361 1
 
3.4%
0.4918032787 2
6.9%
0.5081967213 1
 
3.4%
0.5327868852 3
10.3%
0.5491803279 1
 
3.4%
ValueCountFrequency (%)
0.8524590164 1
 
3.4%
0.8360655738 1
 
3.4%
0.8196721311 1
 
3.4%
0.8114754098 1
 
3.4%
0.7459016393 1
 
3.4%
0.737704918 1
 
3.4%
0.6885245902 2
6.9%
0.6393442623 3
10.3%
0.6147540984 1
 
3.4%
0.5573770492 3
10.3%

Ash_Alcanity
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62439983
Minimum0.46202532
Maximum0.84177215
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-29T07:00:11.080230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.46202532
5-th percentile0.50632911
Q10.55696203
median0.58860759
Q30.6835443
95-th percentile0.82911392
Maximum0.84177215
Range0.37974684
Interquartile range (IQR)0.12658228

Descriptive statistics

Standard deviation0.10494661
Coefficient of variation (CV)0.16807598
Kurtosis-0.35982039
Mean0.62439983
Median Absolute Deviation (MAD)0.063291139
Skewness0.77214113
Sum18.107595
Variance0.011013791
MonotonicityNot monotonic
2023-11-29T07:00:11.359398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.5569620253 8
27.6%
0.6202531646 4
13.8%
0.5253164557 3
 
10.3%
0.6518987342 2
 
6.9%
0.746835443 2
 
6.9%
0.8417721519 2
 
6.9%
0.582278481 1
 
3.4%
0.8101265823 1
 
3.4%
0.7784810127 1
 
3.4%
0.4620253165 1
 
3.4%
Other values (4) 4
13.8%
ValueCountFrequency (%)
0.4620253165 1
 
3.4%
0.4936708861 1
 
3.4%
0.5253164557 3
 
10.3%
0.5569620253 8
27.6%
0.582278481 1
 
3.4%
0.5886075949 1
 
3.4%
0.6202531646 4
13.8%
0.6518987342 2
 
6.9%
0.6835443038 1
 
3.4%
0.7151898734 1
 
3.4%
ValueCountFrequency (%)
0.8417721519 2
6.9%
0.8101265823 1
 
3.4%
0.7784810127 1
 
3.4%
0.746835443 2
6.9%
0.7151898734 1
 
3.4%
0.6835443038 1
 
3.4%
0.6518987342 2
6.9%
0.6202531646 4
13.8%
0.5886075949 1
 
3.4%
0.582278481 1
 
3.4%

Magnesium
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41918103
Minimum0.15625
Maximum0.78125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-29T07:00:11.642115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.15625
5-th percentile0.25
Q10.296875
median0.40625
Q30.515625
95-th percentile0.725
Maximum0.78125
Range0.625
Interquartile range (IQR)0.21875

Descriptive statistics

Standard deviation0.1546835
Coefficient of variation (CV)0.36901358
Kurtosis0.26910313
Mean0.41918103
Median Absolute Deviation (MAD)0.109375
Skewness0.76569562
Sum12.15625
Variance0.023926984
MonotonicityNot monotonic
2023-11-29T07:00:11.902594image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.28125 3
 
10.3%
0.296875 3
 
10.3%
0.40625 3
 
10.3%
0.5 2
 
6.9%
0.25 2
 
6.9%
0.515625 2
 
6.9%
0.34375 2
 
6.9%
0.78125 2
 
6.9%
0.15625 1
 
3.4%
0.421875 1
 
3.4%
Other values (8) 8
27.6%
ValueCountFrequency (%)
0.15625 1
 
3.4%
0.25 2
6.9%
0.28125 3
10.3%
0.296875 3
10.3%
0.3125 1
 
3.4%
0.328125 1
 
3.4%
0.34375 2
6.9%
0.390625 1
 
3.4%
0.40625 3
10.3%
0.421875 1
 
3.4%
ValueCountFrequency (%)
0.78125 2
6.9%
0.640625 1
 
3.4%
0.578125 1
 
3.4%
0.5625 1
 
3.4%
0.546875 1
 
3.4%
0.515625 2
6.9%
0.5 2
6.9%
0.484375 1
 
3.4%
0.421875 1
 
3.4%
0.40625 3
10.3%

Total_Phenols
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.21676576
Minimum0
Maximum0.46206897
Zeros1
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-29T07:00:12.170170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.062068966
Q10.14137931
median0.19655172
Q30.28275862
95-th percentile0.42068966
Maximum0.46206897
Range0.46206897
Interquartile range (IQR)0.14137931

Descriptive statistics

Standard deviation0.10888319
Coefficient of variation (CV)0.50230809
Kurtosis0.33446938
Mean0.21676576
Median Absolute Deviation (MAD)0.055172414
Skewness0.4504747
Sum6.2862069
Variance0.01185555
MonotonicityNot monotonic
2023-11-29T07:00:12.495250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.1724137931 2
 
6.9%
0.2413793103 2
 
6.9%
0.1965517241 2
 
6.9%
0.2482758621 2
 
6.9%
0.1413793103 2
 
6.9%
0.2827586207 2
 
6.9%
0.4551724138 1
 
3.4%
0.2310344828 1
 
3.4%
0.2103448276 1
 
3.4%
0.09310344828 1
 
3.4%
Other values (13) 13
44.8%
ValueCountFrequency (%)
0 1
3.4%
0.04137931034 1
3.4%
0.09310344828 1
3.4%
0.1034482759 1
3.4%
0.1275862069 1
3.4%
0.1379310345 1
3.4%
0.1413793103 2
6.9%
0.1448275862 1
3.4%
0.1724137931 2
6.9%
0.1793103448 1
3.4%
ValueCountFrequency (%)
0.4620689655 1
3.4%
0.4551724138 1
3.4%
0.3689655172 1
3.4%
0.3517241379 1
3.4%
0.3275862069 1
3.4%
0.2931034483 1
3.4%
0.2827586207 2
6.9%
0.2482758621 2
6.9%
0.2413793103 2
6.9%
0.2310344828 1
3.4%

Flavanoids
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.066928561
Minimum0
Maximum0.14345992
Zeros1
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-29T07:00:12.886463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02742616
Q10.037974684
median0.067510549
Q30.088607595
95-th percentile0.12236287
Maximum0.14345992
Range0.14345992
Interquartile range (IQR)0.050632911

Descriptive statistics

Standard deviation0.033594292
Coefficient of variation (CV)0.50194254
Kurtosis-0.23415338
Mean0.066928561
Median Absolute Deviation (MAD)0.023206751
Skewness0.33421009
Sum1.9409283
Variance0.0011285764
MonotonicityNot monotonic
2023-11-29T07:00:13.376712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.07594936709 2
 
6.9%
0.03375527426 2
 
6.9%
0.08860759494 2
 
6.9%
0.07172995781 2
 
6.9%
0.02742616034 2
 
6.9%
0.1223628692 2
 
6.9%
0.1434599156 1
 
3.4%
0.0864978903 1
 
3.4%
0.05696202532 1
 
3.4%
0.0358649789 1
 
3.4%
Other values (13) 13
44.8%
ValueCountFrequency (%)
0 1
3.4%
0.02742616034 2
6.9%
0.03164556962 1
3.4%
0.03375527426 2
6.9%
0.0358649789 1
3.4%
0.03797468354 1
3.4%
0.04430379747 1
3.4%
0.04641350211 1
3.4%
0.05063291139 1
3.4%
0.05485232068 1
3.4%
ValueCountFrequency (%)
0.1434599156 1
3.4%
0.1223628692 2
6.9%
0.1054852321 1
3.4%
0.1033755274 1
3.4%
0.0970464135 1
3.4%
0.08860759494 2
6.9%
0.0864978903 1
3.4%
0.07594936709 2
6.9%
0.07383966245 1
3.4%
0.07172995781 2
6.9%

Nonflavanoid_Phenols
Real number (ℝ)

Distinct13
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62654522
Minimum0.45283019
Maximum0.81132075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-29T07:00:13.819254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.45283019
5-th percentile0.46792453
Q10.52830189
median0.64150943
Q30.73584906
95-th percentile0.75471698
Maximum0.81132075
Range0.35849057
Interquartile range (IQR)0.20754717

Descriptive statistics

Standard deviation0.10665914
Coefficient of variation (CV)0.17023374
Kurtosis-1.3163156
Mean0.62654522
Median Absolute Deviation (MAD)0.094339623
Skewness-0.022429422
Sum18.169811
Variance0.011376171
MonotonicityNot monotonic
2023-11-29T07:00:14.245392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.7547169811 5
17.2%
0.5660377358 4
13.8%
0.5094339623 4
13.8%
0.6981132075 3
10.3%
0.4528301887 2
 
6.9%
0.641509434 2
 
6.9%
0.6603773585 2
 
6.9%
0.7358490566 2
 
6.9%
0.6037735849 1
 
3.4%
0.5283018868 1
 
3.4%
Other values (3) 3
10.3%
ValueCountFrequency (%)
0.4528301887 2
6.9%
0.4905660377 1
 
3.4%
0.5094339623 4
13.8%
0.5283018868 1
 
3.4%
0.5660377358 4
13.8%
0.5849056604 1
 
3.4%
0.6037735849 1
 
3.4%
0.641509434 2
6.9%
0.6603773585 2
6.9%
0.6981132075 3
10.3%
ValueCountFrequency (%)
0.8113207547 1
 
3.4%
0.7547169811 5
17.2%
0.7358490566 2
 
6.9%
0.6981132075 3
10.3%
0.6603773585 2
 
6.9%
0.641509434 2
 
6.9%
0.6037735849 1
 
3.4%
0.5849056604 1
 
3.4%
0.5660377358 4
13.8%
0.5283018868 1
 
3.4%

Proanthocyanins
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26179851
Minimum0.054901961
Maximum0.45098039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-29T07:00:14.581939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.054901961
5-th percentile0.090196078
Q10.15294118
median0.24705882
Q30.36862745
95-th percentile0.43058824
Maximum0.45098039
Range0.39607843
Interquartile range (IQR)0.21568627

Descriptive statistics

Standard deviation0.12216648
Coefficient of variation (CV)0.46664316
Kurtosis-1.3588307
Mean0.26179851
Median Absolute Deviation (MAD)0.11372549
Skewness-0.025994746
Sum7.5921569
Variance0.01492465
MonotonicityNot monotonic
2023-11-29T07:00:15.011925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0.4117647059 3
 
10.3%
0.09019607843 2
 
6.9%
0.1529411765 2
 
6.9%
0.368627451 2
 
6.9%
0.1333333333 1
 
3.4%
0.05490196078 1
 
3.4%
0.3921568627 1
 
3.4%
0.2549019608 1
 
3.4%
0.3254901961 1
 
3.4%
0.1254901961 1
 
3.4%
Other values (14) 14
48.3%
ValueCountFrequency (%)
0.05490196078 1
3.4%
0.09019607843 2
6.9%
0.1058823529 1
3.4%
0.1254901961 1
3.4%
0.1333333333 1
3.4%
0.1529411765 2
6.9%
0.1568627451 1
3.4%
0.1764705882 1
3.4%
0.2078431373 1
3.4%
0.2196078431 1
3.4%
ValueCountFrequency (%)
0.4509803922 1
 
3.4%
0.4431372549 1
 
3.4%
0.4117647059 3
10.3%
0.3921568627 1
 
3.4%
0.3882352941 1
 
3.4%
0.368627451 2
6.9%
0.3490196078 1
 
3.4%
0.3294117647 1
 
3.4%
0.3254901961 1
 
3.4%
0.2901960784 1
 
3.4%

Color_Intensity
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60780966
Minimum0.19155844
Maximum0.97186147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-29T07:00:15.558025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.19155844
5-th percentile0.28463203
Q10.39271409
median0.65151515
Q30.83658009
95-th percentile0.95238091
Maximum0.97186147
Range0.78030303
Interquartile range (IQR)0.443866

Descriptive statistics

Standard deviation0.24146496
Coefficient of variation (CV)0.39727068
Kurtosis-1.4117244
Mean0.60780966
Median Absolute Deviation (MAD)0.21861472
Skewness0.0057031565
Sum17.62648
Variance0.058305327
MonotonicityNot monotonic
2023-11-29T07:00:15.966924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.6893939394 2
 
6.9%
0.1915584416 1
 
3.4%
0.8982683983 1
 
3.4%
0.867965368 1
 
3.4%
0.9653679654 1
 
3.4%
0.6515151515 1
 
3.4%
0.6948051948 1
 
3.4%
0.9112554113 1
 
3.4%
0.9329003247 1
 
3.4%
0.4567099567 1
 
3.4%
Other values (18) 18
62.1%
ValueCountFrequency (%)
0.1915584416 1
3.4%
0.2781385281 1
3.4%
0.2943722944 1
3.4%
0.3322510823 1
3.4%
0.3376623377 1
3.4%
0.3593073593 1
3.4%
0.3917748918 1
3.4%
0.3927140861 1
3.4%
0.3939393939 1
3.4%
0.4329004329 1
3.4%
ValueCountFrequency (%)
0.9718614719 1
3.4%
0.9653679654 1
3.4%
0.9329003247 1
3.4%
0.9112554113 1
3.4%
0.8982683983 1
3.4%
0.867965368 1
3.4%
0.8571428571 1
3.4%
0.8365800866 1
3.4%
0.7987012987 1
3.4%
0.7727272727 1
3.4%

Hue
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22381799
Minimum0.06185567
Maximum0.49484536
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-29T07:00:16.446421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.06185567
5-th percentile0.07628866
Q10.11340206
median0.20618557
Q30.29896907
95-th percentile0.44164948
Maximum0.49484536
Range0.43298969
Interquartile range (IQR)0.18556701

Descriptive statistics

Standard deviation0.13024195
Coefficient of variation (CV)0.58191009
Kurtosis-0.7646778
Mean0.22381799
Median Absolute Deviation (MAD)0.092783505
Skewness0.65981674
Sum6.4907216
Variance0.016962964
MonotonicityNot monotonic
2023-11-29T07:00:16.822417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.2268041237 3
 
10.3%
0.4226804124 2
 
6.9%
0.1237113402 2
 
6.9%
0.1030927835 2
 
6.9%
0.09278350515 2
 
6.9%
0.4391752577 1
 
3.4%
0.3092783505 1
 
3.4%
0.1134020619 1
 
3.4%
0.1649484536 1
 
3.4%
0.1443298969 1
 
3.4%
Other values (13) 13
44.8%
ValueCountFrequency (%)
0.0618556701 1
3.4%
0.07216494845 1
3.4%
0.0824742268 1
3.4%
0.09278350515 2
6.9%
0.1030927835 2
6.9%
0.1134020619 1
3.4%
0.1237113402 2
6.9%
0.1340206186 1
3.4%
0.1443298969 1
3.4%
0.1649484536 1
3.4%
ValueCountFrequency (%)
0.4948453608 1
3.4%
0.4432989691 1
3.4%
0.4391752577 1
3.4%
0.4226804124 2
6.9%
0.381443299 1
3.4%
0.3092783505 1
3.4%
0.2989690722 1
3.4%
0.2783505155 1
3.4%
0.2680412371 1
3.4%
0.2474226804 1
3.4%

OD280
Real number (ℝ)

ZEROS 

Distinct22
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17582418
Minimum0
Maximum0.43956044
Zeros1
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-29T07:00:17.237566image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.054212454
Q10.12087912
median0.16117216
Q30.2014652
95-th percentile0.35750916
Maximum0.43956044
Range0.43956044
Interquartile range (IQR)0.080586081

Descriptive statistics

Standard deviation0.095097106
Coefficient of variation (CV)0.54086479
Kurtosis1.5434971
Mean0.17582418
Median Absolute Deviation (MAD)0.04029304
Skewness0.97019385
Sum5.0989011
Variance0.0090434596
MonotonicityNot monotonic
2023-11-29T07:00:17.511774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.1062271062 3
 
10.3%
0.1758241758 3
 
10.3%
0.2014652015 2
 
6.9%
0.1501831502 2
 
6.9%
0.1282051282 2
 
6.9%
0.1355311355 1
 
3.4%
0.1721611722 1
 
3.4%
0.1611721612 1
 
3.4%
0.1318681319 1
 
3.4%
0.2051282051 1
 
3.4%
Other values (12) 12
41.4%
ValueCountFrequency (%)
0 1
 
3.4%
0.02197802198 1
 
3.4%
0.1025641026 1
 
3.4%
0.1062271062 3
10.3%
0.1135531136 1
 
3.4%
0.1208791209 1
 
3.4%
0.1282051282 2
6.9%
0.1318681319 1
 
3.4%
0.1355311355 1
 
3.4%
0.1501831502 2
6.9%
ValueCountFrequency (%)
0.4395604396 1
 
3.4%
0.380952381 1
 
3.4%
0.3223443223 1
 
3.4%
0.2893772894 1
 
3.4%
0.2857142857 1
 
3.4%
0.2161172161 1
 
3.4%
0.2051282051 1
 
3.4%
0.2014652015 2
6.9%
0.1941391941 1
 
3.4%
0.1758241758 3
10.3%

Proline
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)89.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26349058
Minimum0.097717546
Maximum0.42225392
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size464.0 B
2023-11-29T07:00:17.779364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.097717546
5-th percentile0.13980029
Q10.19400856
median0.26533524
Q30.32952924
95-th percentile0.39942939
Maximum0.42225392
Range0.32453638
Interquartile range (IQR)0.13552068

Descriptive statistics

Standard deviation0.087219705
Coefficient of variation (CV)0.33101641
Kurtosis-0.81112097
Mean0.26349058
Median Absolute Deviation (MAD)0.071326676
Skewness0.066687832
Sum7.6412268
Variance0.007607277
MonotonicityNot monotonic
2023-11-29T07:00:18.036294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.1726105563 3
 
10.3%
0.3366619116 2
 
6.9%
0.422253923 1
 
3.4%
0.2831669044 1
 
3.4%
0.4008559201 1
 
3.4%
0.3972895863 1
 
3.4%
0.3295292439 1
 
3.4%
0.272467903 1
 
3.4%
0.1369472183 1
 
3.4%
0.1654778887 1
 
3.4%
Other values (16) 16
55.2%
ValueCountFrequency (%)
0.09771754636 1
 
3.4%
0.1369472183 1
 
3.4%
0.1440798859 1
 
3.4%
0.1654778887 1
 
3.4%
0.1726105563 3
10.3%
0.1940085592 1
 
3.4%
0.2011412268 1
 
3.4%
0.2225392297 1
 
3.4%
0.2296718973 1
 
3.4%
0.2403708987 1
 
3.4%
ValueCountFrequency (%)
0.422253923 1
3.4%
0.4008559201 1
3.4%
0.3972895863 1
3.4%
0.3937232525 1
3.4%
0.3580599144 1
3.4%
0.3366619116 2
6.9%
0.3295292439 1
3.4%
0.3152639087 1
3.4%
0.2974322397 1
3.4%
0.290299572 1
3.4%

kmeans_cluster
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size464.0 B
1
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 29
100.0%

Length

2023-11-29T07:00:18.314630image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-29T07:00:18.600950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 29
100.0%

Most occurring characters

ValueCountFrequency (%)
1 29
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 29
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 29
100.0%

dbsscan_cluster
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size464.0 B
2
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 29
100.0%

Length

2023-11-29T07:00:18.798906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-29T07:00:19.036612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 29
100.0%

Most occurring characters

ValueCountFrequency (%)
2 29
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 29
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 29
100.0%

dbscan_labels
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size464.0 B
2
29 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 29
100.0%

Length

2023-11-29T07:00:19.230690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-29T07:00:19.475788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 29
100.0%

Most occurring characters

ValueCountFrequency (%)
2 29
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 29
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 29
100.0%

Interactions

2023-11-29T07:00:04.793777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:19.964215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:23.030885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:26.226294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:30.148467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:34.202348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:37.445666image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:41.644167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:46.228105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:49.814259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:53.660470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:57.017546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:01.431043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:05.049617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:20.160805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:23.249785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:26.458862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:30.539608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:34.430240image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:37.679474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:41.910917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:46.609169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:50.050772image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:53.923906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:57.240309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:01.779569image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:05.311490image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:20.371599image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:23.517547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:26.677878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:30.891452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:34.669181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:37.896057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:42.179373image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:46.902327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:50.280498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:54.171947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:57.503483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:02.090539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:05.557761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:20.582570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:23.737720image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:26.893110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:31.223156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:34.909981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:38.136595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:42.402670image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:47.224758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:50.563937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:54.430022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:57.785691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:02.366715image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:05.827397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:20.845593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:23.996472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:27.150795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:31.554295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:35.169325image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:38.417856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:42.772957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:47.540639image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:50.960834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:54.715960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:58.109308image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:02.637271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:06.103357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:21.083487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:24.254284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:27.393683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:31.923612image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:35.417954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:38.655757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:43.183661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:47.790368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:51.608559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:54.969981image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:58.435270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:02.874896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:06.323001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:21.329561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:24.511479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:27.708615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:32.314763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:35.648829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:38.875679image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:43.505466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:48.017284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:51.833044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:55.199329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:58.794109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:03.115734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:06.591508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:21.585355image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:24.772265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:27.999260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:32.672274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:35.924320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:39.127983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:43.899692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:48.279835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:52.084699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:55.456643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:59.170556image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:03.364109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:06.870316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:21.830574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:25.011721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:28.309915image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:32.923949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:36.188336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:39.380706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:44.301003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:48.565855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:52.339541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:55.726525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:59.543329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:03.618743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:07.172646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:22.069334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:25.267754image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:28.699348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:33.183664image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:36.472116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:39.635831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:44.697477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:48.836128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:52.626420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:55.979209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:59.934678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:03.866037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:07.445655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:22.328236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:25.540055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:29.062312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:33.450830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:36.730339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:39.887784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:45.110265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:49.103378image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:52.900431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:56.230500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:00.321839image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:04.127111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:07.712470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:22.561282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:25.763407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:29.420192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:33.690141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:36.992801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:40.106722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:45.500807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:49.325263image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:53.150238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:56.468167image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:00.679843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:04.337866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:07.944261image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:22.785651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:25.977690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:29.774363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:33.942319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:37.211269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:40.324588image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:45.897730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:49.575334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:53.391062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T06:59:56.747375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:01.043506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-11-29T07:00:04.553030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-11-29T07:00:19.667080image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
AlcoholAshAsh_AlcanityColor_IntensityFlavanoidsHueMagnesiumMalic_AcidNonflavanoid_PhenolsOD280ProanthocyaninsProlineTotal_Phenols
Alcohol1.0000.2230.2230.2380.1310.060-0.0580.1870.013-0.0490.197-0.1490.226
Ash0.2231.0000.545-0.0580.1730.2870.1470.0610.3890.216-0.060-0.0510.579
Ash_Alcanity0.2230.5451.000-0.1660.1720.2420.1120.1050.2630.0000.2090.0740.339
Color_Intensity0.238-0.058-0.1661.0000.245-0.508-0.137-0.1810.022-0.2530.275-0.0070.198
Flavanoids0.1310.1730.1720.2451.000-0.0470.324-0.024-0.193-0.1440.7160.2550.520
Hue0.0600.2870.242-0.508-0.0471.0000.330-0.065-0.0410.483-0.1430.183-0.074
Magnesium-0.0580.1470.112-0.1370.3240.3301.000-0.264-0.003-0.0180.4520.5830.173
Malic_Acid0.1870.0610.105-0.181-0.024-0.065-0.2641.0000.064-0.269-0.042-0.2710.182
Nonflavanoid_Phenols0.0130.3890.2630.022-0.193-0.041-0.0030.0641.000-0.143-0.023-0.2640.426
OD280-0.0490.2160.000-0.253-0.1440.483-0.018-0.269-0.1431.000-0.409-0.0600.008
Proanthocyanins0.197-0.0600.2090.2750.716-0.1430.452-0.042-0.023-0.4091.0000.4350.276
Proline-0.149-0.0510.074-0.0070.2550.1830.583-0.271-0.264-0.0600.4351.000-0.023
Total_Phenols0.2260.5790.3390.1980.520-0.0740.1730.1820.4260.0080.276-0.0231.000

Missing values

2023-11-29T07:00:08.364321image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-29T07:00:08.938550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Prolinekmeans_clusterdbsscan_clusterdbscan_labels
700.3315790.1955060.4180330.5822780.5156250.0413790.1434600.4528300.4117650.1915580.4391750.2014650.422254122
1360.3210530.8943820.6885250.6202530.2968750.1379310.0274260.7547170.1529410.2781390.2783510.0000000.315264122
1390.4763160.4988760.7459020.8101270.4843750.4620690.0548520.7547170.1568630.3939390.4226800.3223440.222539122
1400.5000000.4651690.8196720.6202530.4062500.1931030.0337550.7547170.1333330.3593070.2989690.3809520.229672122
1410.6131580.4089890.5327870.5569620.2968750.1448280.0337550.4528300.0901960.4675320.2268040.4395600.358060122
1420.6552630.5460670.8360660.7784810.4218750.1965520.0379750.6981130.0549020.3322510.4226800.2893770.172611122
1430.6815790.9460670.5327870.5569620.3437500.3517240.0970460.6415090.2392160.3376620.4432990.2857140.194009122
1450.5605260.6359550.3688520.6202530.5000000.1793100.0443040.5660380.3490200.2943720.1237110.1501830.393723122
1460.7500000.9662920.4344260.5569620.1562500.0000000.0000000.5094340.1058820.3917750.1030930.0219780.097718122
1470.4842110.8696630.6393440.6518990.2500000.2482760.0654010.6415090.1764710.6893940.0618560.2161170.247504122
AlcoholMalic_AcidAshAsh_AlcanityMagnesiumTotal_PhenolsFlavanoidsNonflavanoid_PhenolsProanthocyaninsColor_IntensityHueOD280Prolinekmeans_clusterdbsscan_clusterdbscan_labels
1670.4710530.5910110.4918030.5253160.2812500.1724140.0675110.5094340.2196080.9718610.2474230.1758240.290300122
1680.6710530.4134830.8114750.8417720.5468750.1965520.1054850.4905660.4431370.7987010.2680410.1941390.336662122
1700.3078950.5146070.5081970.4936710.4062500.0931030.0316460.5094340.1254900.4567100.1855670.2051280.165478122
1710.4578950.3707870.4754100.5253160.2500000.1413790.0358650.6603770.0901960.9329000.0927840.1318680.136947122
1720.8236840.3977530.6393440.5569620.3281250.2413790.0759490.5849060.3254900.9112550.1443300.1611720.272468122
1730.7052630.3459520.6147540.5886080.3906250.2413790.0569620.7358490.2549020.6948050.1649480.1721610.329529122
1740.6236840.7123600.6393440.7468350.5000000.2827590.0864980.5660380.3921570.6515150.2268040.1062270.336662122
1750.5894740.7955060.4590160.5569620.7812500.2103450.0738400.5660380.3686270.9653680.1134020.1062270.397290122
1760.5631580.4157300.5491800.5569620.7812500.2310340.0717300.7547170.4117650.8679650.1237110.1282050.400856122
1770.8157890.7550560.8524590.8417720.4062500.3689660.0886080.8113210.3686270.8571430.1340210.1208790.201141122